Dynamic Radio Resource Allocation for Macro-Femto Hybrid Cellular Network Maintaining Fairness
Bibliographic record
Abstract
Macro-Femto Hybrid Cellular Network (MFHCN) has been considered as one of the most promising infrastructure for the upcoming next generation cellular network. Femto-Cell (FC) is very effective when it comes to reducing poor signal coverage. The range of the device might be low but it functions to reduce the traffic congestion of the larger Macro Network as well as provide enhanced data rate for both voice and data transmission. In short, it provides better Quality of Service (QoS), i.e., reduce congestion, increase capacity, reduce system outage, while using minimal power consumption. In this work we propose a suboptimal Resource Allocation algorithm for MFHCN. Multiuser and Multiservice are considered in this proposed model. A priority parameter is introduced to maintain fairness amongst all users. By doing so, we were successful in achieving improved outage probability without compromising the total system throughput.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".